VA project - Point3 Graph

packages <- c('tidyverse', 'ggdist', 'gghalves', 'ggridges', 'patchwork', 
              'dplyr', 'ggplot2', 'plotly', 'sf', 'tmap')
for(p in packages){
  if(!require(p, character.only = T)){
    install.packages(p)
  }
  library(p, character.only = T)
}

resto <- read_rds('resto.rds')
resto_month2 <- read_rds('resto_month2.rds')
pub <- read_rds('pub.rds')
pub_month2 <- read_rds('pub_month2.rds')

buildings <- read_sf("Buildings.csv", 
                     options = "GEOM_POSSIBLE_NAMES=location")

Region <- st_read('/Users/joyce/Desktop/SMU_Lecture/ISSS608_Visual Analytics/VAST-Challenge-2022/Datasets/shapefile/buildings.shp') 

## Reading layer `buildings' from data source 
##   `/Users/joyce/Desktop/SMU_Lecture/ISSS608_Visual Analytics/VAST-Challenge-2022/Datasets/shapefile/buildings.shp' 
##   using driver `ESRI Shapefile'
## Simple feature collection with 1042 features and 5 fields
## Geometry type: POLYGON
## Dimension:     XY
## Bounding box:  xmin: -4762.191 ymin: -30.08359 xmax: 2650 ymax: 7850.037
## CRS:           NA

resto_arrange <- resto %>%
  select(-c(foodCost, weekend_visit, weekday_visit)) %>%
  rename('Id' = 'restaurantId')

resto_arrange <- resto_arrange %>%
  mutate(type = 'restaurant')

pub_arrange <- pub %>%
  select(-c(hourlyCost, weekend_time, weekday_time)) %>%
  rename('Id' = 'pubId')

pub_arrange <- pub_arrange %>%
  mutate(type = 'pub')

total_data <- rbind(resto_arrange, pub_arrange)

write_rds(total_data, "total_data.rds")

total_data <- read_rds('total_data.rds')

plot_ly(total_data, 
                  x = ~type, 
                  y = ~total_earn,
                  split = ~type,
                  type = 'violin',
                  box = list(visible = T),
                  meanline = list(visible = T)) %>%
  layout(xaxis = list(title = 'Business Type'),
         yaxis = list(title = 'Revenue'))

plot_ly(total_data, 
                  x = ~type, 
                  y = ~weekday_earn,
                  split = ~type,
                  type = 'violin',
                  box = list(visible = T),
                  meanline = list(visible = T)) %>%
  layout(xaxis = list(title = 'Business Type'),
         yaxis = list(title = 'Revenue'))

plot_ly(total_data, 
                  x = ~type, 
                  y = ~weekend_earn,
                  split = ~type,
                  type = 'violin',
                  box = list(visible = T),
                  meanline = list(visible = T)) %>%
  layout(xaxis = list(title = 'Business Type'),
         yaxis = list(title = 'Revenue'))

# Convert foreign object into spatial data
total_data_sf <- st_as_sf(total_data)

#buildings <- buildings %>%
  #st_join(Region, join = c(buildingId, bldngId), left = TRUE)

tm_shape(buildings)+
tm_polygons(col = "grey60",
           size = 1,
           border.col = "black",
           border.lwd = 1) +
tm_shape(total_data_sf) +
  tm_symbols(size = 0.5, shape = "type", shapes.labels = c("Restaurant", "Pub"), title.shape = "Venue Type",
             col = "total_earn", style = "cont", title.col = "Revenue")  +
  tm_layout(main.title.size = 1, legend.outside.size = 0.2)

tm_shape(buildings)+
tm_polygons(col = "grey60",
           size = 1,
           border.col = "black",
           border.lwd = 1) +
tm_shape(total_data_sf) +
  tm_symbols(size = 0.5, shape = "type", shapes.labels = c("Restaurant", "Pub"), title.shape = "Venue Type",
             col = "weekday_earn", style = "cont", title.col = "Revenue")  +
  tm_layout(main.title.size = 1, legend.outside.size = 0.2)

tm_shape(buildings)+
tm_polygons(col = "grey60",
           size = 1,
           border.col = "black",
           border.lwd = 1) +
tm_shape(total_data_sf) +
  tm_symbols(size = 0.5, shape = "type", shapes.labels = c("Restaurant", "Pub"), title.shape = "Venue Type",
             col = "weekend_earn", style = "cont", title.col = "Revenue")  +
  tm_layout(main.title.size = 1, legend.outside.size = 0.2)

resto_month_arrange <- resto_month2 %>%
  select(-c(foodCost, visit))

resto_month_arrange <- resto_month_arrange %>%
  mutate(type = 'restaurant')

pub_month_arrange <- pub_month2 %>%
  select(-c(hourlyCost, total_time)) 

pub_month_arrange <- pub_month_arrange %>%
  mutate(type = 'pub')

total_month_data <- rbind(resto_month_arrange, pub_month_arrange)

write_rds(total_month_data, "total_month_data.rds")

total_month_data <- read_rds('total_month_data.rds')

plot_ly(resto_month2, 
                  x = ~month, 
                  y = ~earn,
                  split = ~month,
                  type = 'violin',
                  box = list(visible = T),
                  meanline = list(visible = T)) %>%
  layout(xaxis = list(title = 'Month'),
         yaxis = list(title = 'Revenue'))

plot_ly(pub_month2, 
                  x = ~month, 
                  y = ~earn,
                  split = ~month,
                  type = 'violin',
                  box = list(visible = T),
                  meanline = list(visible = T)) %>%
  layout(xaxis = list(title = 'Mpnth'),
         yaxis = list(title = 'Revenue'))